For this project, you are given two case studies related to operation analytics. In Case Study 1, you are provided with a dataset containing job-related data, and in Case Study 2, you are given three tables with user-related data. Your task is to perform data analysis on these datasets and tables using SQL and derive insights from them. Here are step-by-step hints to solve each question in layman's terms:

Case Study 1:

To calculate the number of jobs reviewed per hour per day for November 2020, you need to group the data by date and hour, and then count the number of unique jobs reviewed for each hour of each day in November 2020.
To calculate the 7-day rolling average of throughput, you need to group the data by date and calculate the number of events per second for each day. Then, you need to calculate the 7-day rolling average of the events per second using a rolling window function. You can use either daily metric or 7-day rolling average based on which one provides more insights.
To calculate the percentage share of each language in the last 30 days, you need to first filter the data to include only the last 30 days' data. Then, you need to group the data by language and count the number of events for each language. Finally, you need to calculate the percentage share of each language using the total number of events.
To display duplicate rows from the table, you can use the GROUP BY clause to group the data by columns that may have duplicate values, and then use the HAVING clause to filter out groups that have more than one row.

Case Study 2:

To calculate the weekly user engagement, you need to group the data by user and calculate the number of events for each week. Then, you need to calculate the average number of events per user per week.
To calculate the user growth for the product, you need to group the data by date and count the number of new users who signed up on each day. Then, you can plot the growth curve based on the cumulative number of users over time.
To calculate the weekly retention of user-sign up cohort, you need to first identify the cohort of users who signed up on a particular date. Then, you need to group the data by week and count the number of users who continued to engage with the product/service in each week after their sign-up week. Finally, you can calculate the retention rate for each week as the percentage of users who continued to engage with the product/service in that week out of the total number of users in the sign-up cohort.
To calculate the weekly engagement per device, you need to group the data by device and week and calculate the number of events for each device in each week. Then, you need to calculate the average number of events per device per week.
To calculate the email engagement metrics, you need to first join the events table with the email_events table on a common user_id column. Then, you need to group the data by email event type and calculate the number of events for each type. Finally, you can calculate the email engagement metrics based on the total number of email events.

Dataset and case study are confusing.